Multi-Objective Evolutionary Optimization of Probabilistic Neural Network

In this paper the major principles to effectively design a parameter-less, multi-objective evolution-ary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-based classifier. These design principles are extracted from experiences, discussed in this paper, which guided the creation of the parameter-less multi-objective evolutionary algorithm, named MO-EPNN (multi-objective evolutionary probabilistic neural network). Furthermore, these design principles are also corroborated by similar principles used for an earlier design of a parameter-less, multi-objective genetic algorithm used to optimize a population of ART (adaptive resonance theory) models, named MO-GART (multi-objective genetically optimized ART); the ART classifier model is another example of an exemplar-based classifier model. MO-EPNN’s performance is compared to other popular classifier models, such as SVM (Support Vector Machines) and CART (Clas-sification and Regression Trees), as well as to an alternate competitive method to genetically optimize the PNN. These comparisons indicate that MO-EPNN’s performance (generalization on unseen data and size) compares favorably to the aforementioned classifier models and to the alternate genetically optimized PNN approach. MO-EPPN’s good performance, and MO-GART’s earlier reported good per-formance, both of whose design relies on the same principles, gives credence to these design principles, delineated in this paper